1. Field of the Invention
The present invention relates to a technique of analyzing a new pattern based on a pattern learned in advance.
2. Description of the Related Art
There is conventionally a machine learning technique of analyzing a new pattern using a learning pattern. A pattern recognition method called a classification tree or a decision tree (non-patent literature 1 (Leo Breiman, Jerome Friedman, Charles J. Stone, and R. A. Olshen, “Classification and Regression Trees”, Chapman & Hall/CRC (1984))) has been proposed and used in many systems. This method is characterized by being able to quickly analyze a pattern using a tree structure and has shown its capability in the days of computers of poor performance.
Note that when the pattern recognition problem is regarded as a pattern identification problem, the type of the pattern to be identified is called “class”. In the following explanation, the term “class” will be used in this sense.
A drawback of the classical classification tree or decision tree as described in non-patent literature 1 is the relatively low recognition performance. To overcome this drawback, a method using a set (ensemble) of classification trees is proposed in, for example, patent literature 1 (U.S. Pat. No. 6,009,199). In this technique, L (L is a constant of 2 or more and normally ranges from 10 to 100) classification trees are created, and all the classification trees are used to implement higher recognition performance.
Non-patent literature 2 (Vincent Lepetit and Pascal Fua, “Keypoint Recognition Using Randomized Trees”, IEEE Transactions on Pattern Analysis and Machine Intelligence (2006), pp. 1465-1479) discloses an example in which the method using a set (ensemble) of classification trees is applied to a computer vision. In this paper, the authors use a (32 pixels×32 pixels) image and create a classification tree based on the luminance values. More specifically, two points on the (32 pixels×32 pixels) image are selected at random at each node of the classification tree, and the luminance values of the pixels are compared, thereby implementing branch. The paper reports that this processing can be executed at a very high speed, and the recognition accuracy is sufficiently high.
In general, using data acquired by a plurality of methods enables to perform identification more accurately than recognition using single data, as is known. For example, non-patent literature 3 (“Object Detection by Joint Feature Based on Relations of Local Features”, Technical Report of IEICE, vol. 108, no. 484, PRMU 2008-247, pp. 43-54, March, 2009) describes that in object detection from a moving image using joint features, images obtained by sensing an identification target at different resolutions, luminance images, space-time information, range information, and the like are combined, thereby improving the accuracy of detecting a human or a vehicle.
However, when applying the identification using data obtained by a plurality of acquisition methods to identification using classification trees, creating the classification trees comprehensively applied to the images obtained by all acquisition units is not realistic because of the enormous size. For example, when performing recognition using a luminance image and a range image, classification trees of 23=8 patterns are created, as compared to one classification tree created to create a two-stage binary tree and perform recognition using only a luminance image.